Power BI: Integrating AI

Power BI is a powerful data analytics and visualization tool that allows business users to monitor data, analyze trends, and make decisions. Every month, Microsoft pushes Power BI updates out to the end users, and the growth of Power BI is a key part of Microsoft’s current strategy. This course showcases existing AI and machine learning capabilities available directly in accessible Power BI functionalities. Data analytics and business analysis expert Helen Wall gives you a useful overview of Power BI, then dives into the steps to configure Power Query and your data model. Helen steps through the essentials of analyzing single variables and shows you the tools and techniques to measure relationships between variables. She highlights visuals you can use to pose and answer questions in Power BI, explains useful techniques to enhance your analysis of time series data, and walks you through some best practices for sharing your analysis.

Learning objectives
Differentiate between various AI and machine learning techniques and their applications within the Power BI ecosystem.
Explain how dimensionality affects data analysis and the relationship between statistics, AI, and machine learning.
Distinguish between exploratory and explanatory data modeling approaches in Power BI.
Configure Power BI Desktop to integrate with R and Azure Cognitive Services for enhanced analytical capabilities.
Implement natural language processing techniques including language detection, key phrase extraction, and sentiment analysis using Azure Cognitive Services.
Develop predictive models using Power BI's built-in forecasting tools and compare them with advanced models like ARIMA and TBATS.
Execute clustering algorithms on various visualization types to identify meaningful groupings in data.
Construct DAX measures to calculate statistical metrics such as correlations, rolling averages, and best-fit lines for regression analysis.
Apply anomaly detection techniques to identify outliers in time series data and determine their root causes.
Utilize AI-powered visuals such as Key Influencer and Decomposition Tree to automatically identify key drivers and insights in complex datasets.
Design parameter-driven what-if scenarios to model different business outcomes and enable interactive data exploration.
Create sophisticated visualizations including violin plots, correlation matrices, and small multiples to effectively communicate statistical distributions and relationships.
Implement natural language Q&A visuals and smart narratives to make data insights accessible to non-technical users.
Develop consolidated dashboards that combine multiple AI-powered visualizations for comprehensive data storytelling.
Deploy and share AI-enhanced reports in the Power BI service with appropriate access controls and data refresh settings.
Prepare and transform complex datasets using Power Query's advanced features including fuzzy matching and AI-powered image recognition.
Model data relationships using the star schema and optimize data aggregations for AI analysis.
Integrate multiple AI techniques within a single Power BI solution to address comprehensive business intelligence needs.
Evaluate the appropriate AI and machine learning techniques to apply based on specific business requirements and data characteristics.